Optimization of pyrazolo[1,5-a]pyrimidines lead to the identification of a highly selective casein kinase 2 inhibitor
Bibliographic record
Abstract
ABSTRACT Casein kinase 2 (CK2) is a constitutively expressed serine/threonine kinase that has a large diversity of cellular substrates. Thus, CK2 has been associated with a plethora of regulatory functions and dysregulation of CK2 has been linked to disease development in particular to cancer. The broad implications in disease pathology makes CK2 an attractive target. To date, the most advanced CK2 inhibitor is silmitasertib, which has been investigated in clinical trials for treatment of various cancers, albeit several off-targets for silmitasertib have been described. To ascertain the role of CK2 inhibition in cancer, other disease and normal physiology the development of a selective CK2 inhibitor would be highly desirable. In this study we explored the pyrazolo[1,5-a]pyrimidine hinge-binding moiety for the development of selective CK2 inhibitors. Optimization of this scaffold, which included macrocyclization, led to IC20 ( 31 ) a compound that displayed high in vitro potency for CK2 ( K D = 12 nM) and exclusive selectivity for CK2. X-ray analysis revealed a canonical type-I binding mode for IC20 . However, the polar carboxylic acid moiety that is shared by many CK2 inhibitors including silmitasertib was required for potency and reduced somewhat cellular activity. In summary, IC20 represents a highly selective and potent inhibitor of CK2, which can be used as a tool compound to study CK2 biology and potential new applications for the treatment of diseases. Notes The authors declare no conflict of interest. TOC Figure / Graphical Abstract
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".